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To maintain human-like active balance in a humanoid robot, this article proposes a dynamic priority-based multitask algorithm to avoid self-collision during highly complex robotic whole-body motions and rebalance after external disturbance using momentum compensation strategies. On the one hand, the conflict between self-collision and self-balance constraints in task-space merging with end-effector tracking tasks are considered in the multitask algorithm to improve the robot’s balance. On the other hand, for self-balance during the robot’s movement, momentum compensation is considered as one task and utilized to reject unknown disturbances. Two strategies are put forward to restrain the sudden change in momentum. One is to calculate the correction in the joint space with the resolved momentum control (RMC) method; the other is to add an end-effector tracking motion in the task space. Simulations and experiments on a full-body humanoid robot validate the effectiveness of the proposed method. Experiments on both a simulation and a full-body humanoid are designed to validate the task-prior algorithm. With the proposed method, the humanoid robot succeeds in avoiding self-collision during movement and is able to rectify itself to the preplanned steady stance while encountering undefined external forces.